agemayjune <- subset(ages, t < 52)
agejulaug <- subset(ages, t > 51 & t < 114)
agesepoct <- subset(ages, t > 113 & t < 166)



set.seed(6575)
mayjunesample <- sample.split(agemayjune$AGE, SplitRatio = 0.80)
mayjunetrain <- subset(agemayjune, mayjunesample == TRUE)
mayjunetest <- subset(agemayjune, mayjunesample == FALSE)

mayjunemod <- polr(riskcat ~ AGE, data = mayjunetrain, Hess = TRUE)

mayjunepred = predict(mayjunemod, mayjunetest)
table(mayjunepred, mayjunetest$riskcat)

set.seed(5767)
julaugsample <- sample.split(agejulaug$AGE, SplitRatio = 0.80)
julaugtrain <- subset(agejulaug, julaugsample == TRUE)
julaugtest <- subset(agejulaug, julaugsample == FALSE)

julaugmod <- polr(riskcat ~ AGE, data = julaugtrain, Hess = TRUE)

julaugpred = predict(julaugmod, julaugtest)
table(julaugpred, julaugtest$riskcat)

set.seed(7656)
sepoctsample <- sample.split(agesepoct$AGE, SplitRatio = 0.80)
sepoctrain <- subset(agesepoct, sepoctsample == TRUE)
sepoctest <- subset(agesepoct, sepoctsample == FALSE)

sepoctmod <- polr(riskcat ~ AGE, data = sepoctrain, Hess = TRUE)

sepoctpred = predict(sepoctmod, sepoctest)
table(sepoctpred, sepoctest$riskcat)

summary(mayjunemod)
summary(julaugmod)
summary(sepoctmod)